Estimating the Resilience of Non-Stationary Systems

TL;DR

This paper introduces a new regression-based method to estimate the resilience of non-stationary Earth system components, addressing challenges posed by seasonal forcing and data gaps.

Contribution

The authors develop a robust, flexible approach that accounts for non-stationarity and data uncertainties, improving resilience estimation without extensive data pre-processing.

Findings

The method effectively handles gaps and irregular sampling in data.

It can incorporate time-varying uncertainties in stability estimates.

The approach is extendable to spatial systems and outperforms autocorrelation-based methods.

Abstract

A wide body of work has applied the concept of critical slowing down to estimate the stability of different Earth system components. Most of them -- such as global vegetation -- are inherently non-stationary, for example due to strong seasonal forcing, which complicates the estimation of their resilience to external perturbations. Here, we introduce a new method to account for non-stationarity in estimating resilience for diverse synthetic and real-world data sets via a regression-based formulation of the Langevin Equation. Our method does not require extensive data pre-processing, is robust to gaps in the data record, and does not require regular time sampling. We further show that our method can incorporate time-varying data uncertainties, recover uncertainty bounds in stability estimates, and can be natively extended to examine spatial systems. Our method is a drop-in replacement for widely-used autocorrelation-based resilience estimates, and can be widely applied across Earth system components.